ABSTRACT

Current mass individualisation and service-oriented paradigm calls for high flexibility and agility in the warehouse system to adapt changes in products. This paper proposes a novel digital twin-driven joint optimisation approach for warehousing in large-scale automated high-rise warehouse product-service system. A Digital Twin System is developed to aggregate real-time data from physical warehouse product-service system and then to map it to the cyber model. A joint optimisation model on how to timely optimise stacked packing and storage assignment of warehouse product-service system is integrated to the Digital Twin System. Through perceiving online data from the physical warehouse product-service system, periodical optimal decisions can be obtained via the joint optimisation model and then fed back to the semi-physical simulation engine in the Digital Twin System for verifying the implementation result. A demonstrative prototype is developed and verified with a case study of a tobacco warehouse product-service system. The proposed approach can maximise the utilisation and efficiency of the large-scale automated high-rise warehouse product-service system.

摘要

当前的大规模个性化和面向服务的范式要求仓库系统具有高度的灵活性和敏捷性，以适应产品的变化。本文提出了一种新的数字孪生驱动联合优化方法，用于大型自动化高层仓库产品服务系统中的仓储。开发了一个数字孪生系统来聚合来自物理仓库产品服务系统的实时数据，然后将其映射到网络模型。关于如何及时优化仓库产品服务系统的堆叠包装和存储分配的联合优化模型被集成到数字孪生系统中。通过从实体仓库产品服务系统感知在线数据，可以通过联合优化模型获得周期性的最优决策，然后反馈到数字孪生系统中的半物理仿真引擎，以验证实施结果。通过烟草仓库产品服务系统的案例研究开发并验证了一个示范原型。所提出的方法可以最大限度地提高大型自动化高层仓库产品服务系统的利用率和效率。